





"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		BertModel				,		SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

      lowerCAmelCase    		=			(
          ("""layer.""", """layer_"""),
          ("""word_embeddings.weight""", """word_embeddings"""),
          ("""position_embeddings.weight""", """position_embeddings"""),
          ("""token_type_embeddings.weight""", """token_type_embeddings"""),
          (""".""", """/"""),
          ("""LayerNorm/weight""", """LayerNorm/gamma"""),
          ("""LayerNorm/bias""", """LayerNorm/beta"""),
          ("""weight""", """kernel"""),
      )

      if not os.path.isdir(SCREAMING_SNAKE_CASE     ):
            os.makedirs(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			model.state_dict()

      def to_tf_var_name(SCREAMING_SNAKE_CASE	:		str     ):
            for patt, repl in iter(SCREAMING_SNAKE_CASE     ):
                  lowerCAmelCase    		=			name.replace(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            return F'bert/{name}'

      def create_tf_var(SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		tf.Session     ):
            lowerCAmelCase    		=			tf.dtypes.as_dtype(tensor.dtype     )
            lowerCAmelCase    		=			tf.get_variable(dtype=SCREAMING_SNAKE_CASE				,		shape=tensor.shape				,		name=SCREAMING_SNAKE_CASE				,		initializer=tf.zeros_initializer()     )
            session.run(tf.variables_initializer([tf_var]     )     )
            session.run(SCREAMING_SNAKE_CASE     )
            return tf_var

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  lowerCAmelCase    		=			to_tf_var_name(SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose     ):
                        lowerCAmelCase    		=			torch_tensor.T
                  lowerCAmelCase    		=			create_tf_var(tensor=SCREAMING_SNAKE_CASE				,		name=SCREAMING_SNAKE_CASE				,		session=SCREAMING_SNAKE_CASE     )
                  tf.keras.backend.set_value(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			session.run(SCREAMING_SNAKE_CASE     )
                  print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}'     )

            lowerCAmelCase    		=			tf.train.Saver(tf.trainable_variables()     )
            saver.save(SCREAMING_SNAKE_CASE				,		os.path.join(SCREAMING_SNAKE_CASE				,		model_name.replace("""-"""				,		"""_"""     ) + """.ckpt"""     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ):







      '''simple docstring'''




      lowerCAmelCase    		=			argparse.ArgumentParser()
      parser.add_argument("""--model_name"""				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""model name e.g. bert-base-uncased"""     )
      parser.add_argument(
          """--cache_dir"""				,		type=SCREAMING_SNAKE_CASE				,		default=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""Directory containing pytorch model"""     )
      parser.add_argument("""--pytorch_model_path"""				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""/path/to/<pytorch-model-name>.bin"""     )
      parser.add_argument("""--tf_cache_dir"""				,		type=SCREAMING_SNAKE_CASE				,		required=SCREAMING_SNAKE_CASE				,		help="""Directory in which to save tensorflow model"""     )
      lowerCAmelCase    		=			parser.parse_args(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			BertModel.from_pretrained(
          pretrained_model_name_or_path=args.model_name				,		state_dict=torch.load(args.pytorch_model_path     )				,		cache_dir=args.cache_dir				,		)

      convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name     )


if __name__ == "__main__":
       main()













"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






